AI summary

This paper introduces a deep convolutional neural network architecture for various NLP tasks, trained jointly with weight-sharing. The model uses unlabeled data with multitask and semi-supervised learning, achieving state-of-the-art results in semantic role labeling on the PropBank dataset without using POS tags or parse tree features.

Abstract

We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model. The entire network is trained jointly on all these tasks using weight-sharing, an instance of multitask learning. All the tasks use labeled data except the language model which is learnt from unlabeled text and represents a novel form of semi-supervised learning for the shared tasks. We show how both multitask learning and semi-supervised learning improve the generalization of the shared tasks, resulting in state-of-the-art performance.